Analog quantum approximate optimization algorithm

N., Barraza,; Barrios, Gabriel Dario Alvarado; Peng, Jie; Lamata, Lucas; Solano, E.; Albarrán-Arriagada, F.

doi:10.1088/2058-9565/ac91f0

Cited by 9 publications

(4 citation statements)

References 36 publications

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“…From an experimental standpoint, the amount of possible operations is often very limited and usually involves scenarios where the qubits are arranged in a certain specific manner only allowing a subset of the totality of the interactions (e.g. [45,63] where a certain ansatz for A CD is considered, or [64], where different graphs illustrating some connectivities are depicted). Consequently, trying to solve the entirety of the CD protocol in such situations would be impractical and unrealistic.…”

Section: Scalabilitymentioning

confidence: 99%

Physics-informed neural networks for an optimal counterdiabatic quantum computation

Ferrer-Sánchez,

Flores-Garrigos,

Hernani-Morales

et al. 2024

Mach. Learn.: Sci. Technol.

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A novel methodology that leverages Physics-Informed Neural Networks to optimize quantum circuits in systems with NQ qubits by addressing the counterdiabatic protocol is introduced. The primary purpose is to employ physics-inspired deep learning techniques for accurately modeling the time evolution of various physical observables within quantum systems. To achieve this, we integrate essential physical information into an underlying neural network to effectively tackle the problem. Specifically, the imposition of the solution to meet the principle of least action, along with the hermiticity condition on all physical observables, among others, ensuring the acquisition of appropriate counterdiabatic terms based on underlying physics. This approach provides a reliable alternative to previous methodologies relying on classical numerical approximations, eliminating their inherent constraints. The proposed method offers a versatile framework for optimizing physical observables relevant to the problem, such as the scheduling function, gauge potential, temporal evolution of energy levels, among others. This methodology has been successfully applied to 2-qubit representing H2 molecule using the STO-3G basis, demonstrating the derivation of a desirable decomposition for non-adiabatic terms through a linear combination of Pauli operators. This attribute confers significant advantages for practical implementation within quantum computing algorithms.

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Section: Scalabilitymentioning

confidence: 99%

Physics-informed neural networks for an optimal counterdiabatic quantum computation

Ferrer-Sánchez,

Flores-Garrigos,

Hernani-Morales

et al. 2024

Mach. Learn.: Sci. Technol.

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“…Our previous explorations focused on the classification problem for relatively small quantum systems (N = 2, 3, 4) using both GSK and DSK and fixing the amplitude of the magnetic field h. One crucial point is how our approach can deal with a high-dimensional Hilbert space [33]. By increasing the number of qubits, we are dealing with computational aspects in terms of resources and physical phenomena like the Anderson orthogonality catastrophe [26] for the GSK method.…”

Section: Increasing Number Of Qubitsmentioning

confidence: 99%

Quantum kernels for classifying dynamical singularities in a multiqubit system

Tancara,

Fredes,

Norambuena

2024

Quantum Sci. Technol.

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Dynamical quantum phase transition is a critical phenomenon involving out-of-equilibrium states and broken symmetries without classical analogy. However, when finite-sized systems are analyzed, dynamical singularities of the rate function can appear, leading to a challenging physical characterization when parameters are changed. Here, we report a quantum support vector machine (QSVM) algorithm that uses quantum Kernels to classify dynamical singularities of the rate function for a multiqubit system. We illustrate our approach using $N$ long-range interacting qubits subjected to an arbitrary magnetic field, which induces a quench dynamics. Inspired by physical arguments, we introduce two different quantum Kernels, one inspired by the ground state manifold and the other based on a single state tomography. Our accuracy and adaptability results show that this quantum dynamical critical problem can be efficiently solved using physically inspiring quantum Kernels. Moreover, we extend our results for the case of time-dependent fields, quantum master equation, and when we increase the number of qubits

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“…Since they implicitly translate the data into a higher-dimensional space using a kernel function, they are very helpful for handling high-dimensional feature spaces. Although computing this mapping can be costly, quantum computing allows for effective computation (Schuld et al, 2020) The quantum approximate optimization method (QAOA) was described by Barraza et al, 2022;, a method that combines conventional and quantum principles to address combinatorial optimization problems. Encoding the data into a graph and utilizing the QAOA to identify a low-energy state of the graph may also be modified to handle huge datasets with high-dimensional feature spaces.…”

Section: Rq4mentioning

confidence: 99%

Variational Quantum-Classical Algorithms: A Review of Theory, Applications, and Opportunities

Adebayo,

Basaky,

Osaghae

2023

USci

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Variational Quantum-Classical Algorithm (VQCA) is a potential tool for machine learning (ML) prediction tasks, but its efficacy, adaptability to big datasets, and optimization for noise reduction on quantum hardware are not clear. We aim to accomplish three study goals in this literature review. We begin by reviewing the justifications for ML practitioners' use of VQCA. Second, we compare the accuracy and effectiveness of VQCA in diverse domains to see whether it has a performance advantage over other ML methods. Finally, we evaluate VQCA's immediate and long-term effects on quantum ML and how well it performs compared to ML techniques for prediction tasks across various applications or domains. Our findings show that VQCA can be significantly more accurate and efficient than conventional algorithms. We also compare traditional ML algorithms with VQCA on various datasets and examine their theoretical guarantees. We equally look into how VQCA might be used practically to address problems in a variety of industries, including banking, healthcare, and energy. In various datasets, we assess the performance and efficacy of VQCA for unsupervised learning tasks. Finally, we go through ways to improve VQCA, particularly for big and complicated problems, to lessen the effect of noise and other sources of error in quantum hardware. Overall, we looked at VQCA’s advantages and disadvantages for ML prediction tasks, including possible directions for future study. Our findings show that VQCA has the potential to completely transform the ML industry, particularly in this emerging era of quantum computing.

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Analog quantum approximate optimization algorithm

Cited by 9 publications

References 36 publications

Physics-informed neural networks for an optimal counterdiabatic quantum computation

Physics-informed neural networks for an optimal counterdiabatic quantum computation

Quantum kernels for classifying dynamical singularities in a multiqubit system

Variational Quantum-Classical Algorithms: A Review of Theory, Applications, and Opportunities

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